Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations309392
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.1 MiB
Average record size in memory190.0 B

Variable types

Numeric8
DateTime1
Categorical1

Alerts

Patient_ID is highly overall correlated with Unnamed: 0High correlation
Unnamed: 0 is highly overall correlated with Patient_IDHigh correlation
calories is highly overall correlated with stepsHigh correlation
heart_rate is highly overall correlated with stepsHigh correlation
steps is highly overall correlated with calories and 1 other fieldsHigh correlation
carb_input is highly skewed (γ1 = 61.34612753) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
calories has 7200 (2.3%) zeros Zeros
steps has 219350 (70.9%) zeros Zeros
basal_rate has 115451 (37.3%) zeros Zeros
bolus_volume_delivered has 305801 (98.8%) zeros Zeros
carb_input has 306746 (99.1%) zeros Zeros

Reproduction

Analysis started2025-08-20 20:11:41.031540
Analysis finished2025-08-20 20:11:47.398409
Duration6.37 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct309392
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154695.5
Minimum0
Maximum309391
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:47.456330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15469.55
Q177347.75
median154695.5
Q3232043.25
95-th percentile293921.45
Maximum309391
Range309391
Interquartile range (IQR)154695.5

Descriptive statistics

Standard deviation89313.922
Coefficient of variation (CV)0.57735307
Kurtosis-1.2
Mean154695.5
Median Absolute Deviation (MAD)77348
Skewness0
Sum4.786155 × 1010
Variance7.9769766 × 109
MonotonicityStrictly increasing
2025-08-20T16:11:47.516149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
206267 1
 
< 0.1%
206265 1
 
< 0.1%
206264 1
 
< 0.1%
206263 1
 
< 0.1%
206262 1
 
< 0.1%
206261 1
 
< 0.1%
206260 1
 
< 0.1%
206259 1
 
< 0.1%
206258 1
 
< 0.1%
Other values (309382) 309382
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
309391 1
< 0.1%
309390 1
< 0.1%
309389 1
< 0.1%
309388 1
< 0.1%
309387 1
< 0.1%
309386 1
< 0.1%
309385 1
< 0.1%
309384 1
< 0.1%
309383 1
< 0.1%
309382 1
< 0.1%

time
Date

Distinct228802
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Minimum2018-06-13 18:40:00
Maximum2022-05-18 12:15:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-20T16:11:47.570232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:47.626563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

glucose
Real number (ℝ)

Distinct23077
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.42505
Minimum40
Maximum444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:47.678162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile66
Q199.666667
median132
Q3173
95-th percentile251.33333
Maximum444
Range404
Interquartile range (IQR)73.333333

Descriptive statistics

Standard deviation57.085587
Coefficient of variation (CV)0.40364551
Kurtosis1.0182307
Mean141.42505
Median Absolute Deviation (MAD)36
Skewness0.93078647
Sum43755779
Variance3258.7642
MonotonicityNot monotonic
2025-08-20T16:11:47.732105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 1330
 
0.4%
114 1324
 
0.4%
123 1304
 
0.4%
111 1300
 
0.4%
121 1297
 
0.4%
124 1293
 
0.4%
117 1283
 
0.4%
119 1275
 
0.4%
118 1272
 
0.4%
106 1270
 
0.4%
Other values (23067) 296444
95.8%
ValueCountFrequency (%)
40 1175
0.4%
40.33333333 17
 
< 0.1%
40.42857143 1
 
< 0.1%
40.66666667 35
 
< 0.1%
41 58
 
< 0.1%
41.08130081 1
 
< 0.1%
41.16260163 1
 
< 0.1%
41.24390244 1
 
< 0.1%
41.32520325 1
 
< 0.1%
41.33333333 39
 
< 0.1%
ValueCountFrequency (%)
444 1
< 0.1%
443.5 1
< 0.1%
442.6666667 1
< 0.1%
441.8333333 1
< 0.1%
441 1
< 0.1%
439 1
< 0.1%
438 1
< 0.1%
437 1
< 0.1%
433.3333333 1
< 0.1%
430.6666667 1
< 0.1%

calories
Real number (ℝ)

High correlation  Zeros 

Distinct24133
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8135679
Minimum0
Maximum106.35
Zeros7200
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:47.786192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.217
Q15.8460999
median6.2780998
Q39.1930598
95-th percentile22.8966
Maximum106.35
Range106.35
Interquartile range (IQR)3.3469599

Descriptive statistics

Standard deviation6.9304488
Coefficient of variation (CV)0.78633862
Kurtosis12.030762
Mean8.8135679
Median Absolute Deviation (MAD)1.6003001
Skewness3.0276378
Sum2726847.4
Variance48.031121
MonotonicityNot monotonic
2025-08-20T16:11:47.839712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.154999733 42517
 
13.7%
6.17200017 20321
 
6.6%
4.496999979 9117
 
2.9%
0 7200
 
2.3%
4.234499931 6029
 
1.9%
4.217000008 5200
 
1.7%
6.295440078 4996
 
1.6%
6.278099775 4357
 
1.4%
4.586939991 3082
 
1.0%
6.278099775 3014
 
1.0%
Other values (24123) 203559
65.8%
ValueCountFrequency (%)
0 7200
2.3%
3.918499947 856
 
0.3%
3.996869981 191
 
0.1%
4.025000036 1669
 
0.5%
4.068500102 814
 
0.3%
4.075239956 10
 
< 0.1%
4.075240016 88
 
< 0.1%
4.105500042 327
 
0.1%
4.149870098 188
 
0.1%
4.153609991 6
 
< 0.1%
ValueCountFrequency (%)
106.3500004 1
< 0.1%
98.83459854 2
< 0.1%
98.69279861 1
< 0.1%
97.84200001 1
< 0.1%
92.45359993 1
< 0.1%
91.7446003 1
< 0.1%
89.33399963 1
< 0.1%
89.05039978 1
< 0.1%
88.9085989 1
< 0.1%
86.4980011 2
< 0.1%

heart_rate
Real number (ℝ)

High correlation 

Distinct155668
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.990001
Minimum32.407773
Maximum195.61538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:47.890673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32.407773
5-th percentile56.319672
Q164.930233
median75.418726
Q385.612685
95-th percentile105.99631
Maximum195.61538
Range163.20761
Interquartile range (IQR)20.682453

Descriptive statistics

Standard deviation15.546699
Coefficient of variation (CV)0.2019314
Kurtosis0.95595354
Mean76.990001
Median Absolute Deviation (MAD)10.35812
Skewness0.83484325
Sum23820090
Variance241.69984
MonotonicityNot monotonic
2025-08-20T16:11:47.944972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 430
 
0.1%
74 181
 
0.1%
71 167
 
0.1%
72 166
 
0.1%
77 164
 
0.1%
81 162
 
0.1%
82 159
 
0.1%
76 158
 
0.1%
64 157
 
0.1%
80 155
 
0.1%
Other values (155658) 307493
99.4%
ValueCountFrequency (%)
32.40777256 1
< 0.1%
32.51501909 1
< 0.1%
33.05195711 1
< 0.1%
33.42267674 1
< 0.1%
34.39859267 1
< 0.1%
35.17972556 1
< 0.1%
36.3986992 1
< 0.1%
37.11538462 1
< 0.1%
37.36363636 1
< 0.1%
37.83514559 1
< 0.1%
ValueCountFrequency (%)
195.6153846 1
< 0.1%
185.1153846 1
< 0.1%
181.1785714 1
< 0.1%
178.7619048 1
< 0.1%
178.3529412 1
< 0.1%
178.3467742 1
< 0.1%
177.5777778 1
< 0.1%
177.5384615 1
< 0.1%
176.826087 1
< 0.1%
175.7631579 1
< 0.1%

steps
Real number (ℝ)

High correlation  Zeros 

Distinct706
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.825005
Minimum0
Maximum842
Zeros219350
Zeros (%)70.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:47.998877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile191
Maximum842
Range842
Interquartile range (IQR)11

Descriptive statistics

Standard deviation84.981109
Coefficient of variation (CV)2.7568887
Kurtosis17.531028
Mean30.825005
Median Absolute Deviation (MAD)0
Skewness3.9790262
Sum9537010
Variance7221.7888
MonotonicityNot monotonic
2025-08-20T16:11:48.145738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219350
70.9%
7 2309
 
0.7%
8 2168
 
0.7%
6 1849
 
0.6%
9 1832
 
0.6%
10 1551
 
0.5%
11 1328
 
0.4%
4 1328
 
0.4%
12 1226
 
0.4%
15 1213
 
0.4%
Other values (696) 75238
 
24.3%
ValueCountFrequency (%)
0 219350
70.9%
1 118
 
< 0.1%
2 107
 
< 0.1%
3 82
 
< 0.1%
4 1328
 
0.4%
5 918
 
0.3%
6 1849
 
0.6%
7 2309
 
0.7%
8 2168
 
0.7%
9 1832
 
0.6%
ValueCountFrequency (%)
842 1
< 0.1%
790 1
< 0.1%
786 1
< 0.1%
785 1
< 0.1%
784 1
< 0.1%
780 1
< 0.1%
779 1
< 0.1%
776 1
< 0.1%
775 1
< 0.1%
769 1
< 0.1%

basal_rate
Real number (ℝ)

Zeros 

Distinct115
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041323558
Minimum0
Maximum0.25
Zeros115451
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:48.195343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.056
Q30.066
95-th percentile0.0875
Maximum0.25
Range0.25
Interquartile range (IQR)0.066

Descriptive statistics

Standard deviation0.036106152
Coefficient of variation (CV)0.87374256
Kurtosis0.63971123
Mean0.041323558
Median Absolute Deviation (MAD)0.017333333
Skewness0.42914736
Sum12785.178
Variance0.0013036542
MonotonicityNot monotonic
2025-08-20T16:11:48.248612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115451
37.3%
0.066 38379
 
12.4%
0.069 32257
 
10.4%
0.059 15623
 
5.0%
0.056 13521
 
4.4%
0.062 12864
 
4.2%
0.052 11037
 
3.6%
0.042 9564
 
3.1%
0.073 5346
 
1.7%
0.075 5053
 
1.6%
Other values (105) 50297
16.3%
ValueCountFrequency (%)
0 115451
37.3%
0.009166666667 12
 
< 0.1%
0.01 9
 
< 0.1%
0.01666666667 74
 
< 0.1%
0.02083333333 6
 
< 0.1%
0.021 2155
 
0.7%
0.025 546
 
0.2%
0.02916666667 519
 
0.2%
0.03 41
 
< 0.1%
0.03041666667 34
 
< 0.1%
ValueCountFrequency (%)
0.25 156
 
0.1%
0.2416666667 148
 
< 0.1%
0.2333333333 149
 
< 0.1%
0.1833333333 171
 
0.1%
0.1666666667 48
 
< 0.1%
0.1583333333 560
0.2%
0.152 324
0.1%
0.15 567
0.2%
0.146 63
 
< 0.1%
0.1416666667 590
0.2%

bolus_volume_delivered
Real number (ℝ)

Zeros 

Distinct163
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066077016
Minimum0
Maximum19.8
Zeros305801
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:48.299660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19.8
Range19.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75504733
Coefficient of variation (CV)11.426777
Kurtosis193.49255
Mean0.066077016
Median Absolute Deviation (MAD)0
Skewness13.437799
Sum20443.7
Variance0.57009647
MonotonicityNot monotonic
2025-08-20T16:11:48.354794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 305801
98.8%
2 532
 
0.2%
10 429
 
0.1%
3 319
 
0.1%
12 314
 
0.1%
1 287
 
0.1%
8 238
 
0.1%
4 143
 
< 0.1%
9 118
 
< 0.1%
5 110
 
< 0.1%
Other values (153) 1101
 
0.4%
ValueCountFrequency (%)
0 305801
98.8%
0.03 1
 
< 0.1%
0.1 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 6
 
< 0.1%
0.25 1
 
< 0.1%
0.3 8
 
< 0.1%
0.35 4
 
< 0.1%
0.38 1
 
< 0.1%
0.4 14
 
< 0.1%
ValueCountFrequency (%)
19.8 1
< 0.1%
18.8 1
< 0.1%
18 2
< 0.1%
17.6 1
< 0.1%
17.2 1
< 0.1%
17 1
< 0.1%
16.8 1
< 0.1%
16.4 1
< 0.1%
16.2 1
< 0.1%
16.1 1
< 0.1%

carb_input
Real number (ℝ)

Skewed  Zeros 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05271775
Minimum0
Maximum130
Zeros306746
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2025-08-20T16:11:48.409179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum130
Range130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5054328
Coefficient of variation (CV)28.556469
Kurtosis4176.2843
Mean0.05271775
Median Absolute Deviation (MAD)0
Skewness61.346128
Sum16310.45
Variance2.2663279
MonotonicityNot monotonic
2025-08-20T16:11:48.462308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 306746
99.1%
1 456
 
0.1%
4 425
 
0.1%
2 361
 
0.1%
3 316
 
0.1%
5 300
 
0.1%
6 226
 
0.1%
0.5 78
 
< 0.1%
7 75
 
< 0.1%
1.5 72
 
< 0.1%
Other values (44) 337
 
0.1%
ValueCountFrequency (%)
0 306746
99.1%
0.5 78
 
< 0.1%
1 456
 
0.1%
1.25 2
 
< 0.1%
1.5 72
 
< 0.1%
1.6 3
 
< 0.1%
2 361
 
0.1%
2.1 1
 
< 0.1%
2.25 1
 
< 0.1%
2.3 1
 
< 0.1%
ValueCountFrequency (%)
130 1
 
< 0.1%
120 6
 
< 0.1%
110 17
< 0.1%
105 1
 
< 0.1%
103 1
 
< 0.1%
101 1
 
< 0.1%
100 13
< 0.1%
90 9
< 0.1%
80 2
 
< 0.1%
79 1
 
< 0.1%

Patient_ID
Categorical

High correlation 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 MiB
HUPA0027P
165306 
HUPA0026P
40605 
HUPA0028P
25902 
HUPA0001P
 
4096
HUPA0022P
 
4023
Other values (20)
69460 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters2784528
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHUPA0023P
2nd rowHUPA0023P
3rd rowHUPA0023P
4th rowHUPA0023P
5th rowHUPA0023P

Common Values

ValueCountFrequency (%)
HUPA0027P 165306
53.4%
HUPA0026P 40605
 
13.1%
HUPA0028P 25902
 
8.4%
HUPA0001P 4096
 
1.3%
HUPA0022P 4023
 
1.3%
HUPA0025P 4006
 
1.3%
HUPA0023P 3919
 
1.3%
HUPA0018P 3895
 
1.3%
HUPA0005P 3858
 
1.2%
HUPA0007P 3857
 
1.2%
Other values (15) 49925
 
16.1%

Length

2025-08-20T16:11:48.510086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hupa0027p 165306
53.4%
hupa0026p 40605
 
13.1%
hupa0028p 25902
 
8.4%
hupa0001p 4096
 
1.3%
hupa0022p 4023
 
1.3%
hupa0025p 4006
 
1.3%
hupa0023p 3919
 
1.3%
hupa0018p 3895
 
1.3%
hupa0005p 3858
 
1.2%
hupa0007p 3857
 
1.2%
Other values (15) 49925
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 652670
23.4%
P 618784
22.2%
H 309392
11.1%
U 309392
11.1%
A 309392
11.1%
2 259072
 
9.3%
7 172762
 
6.2%
6 46730
 
1.7%
1 39754
 
1.4%
8 29797
 
1.1%
Other values (4) 36783
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2784528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 652670
23.4%
P 618784
22.2%
H 309392
11.1%
U 309392
11.1%
A 309392
11.1%
2 259072
 
9.3%
7 172762
 
6.2%
6 46730
 
1.7%
1 39754
 
1.4%
8 29797
 
1.1%
Other values (4) 36783
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2784528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 652670
23.4%
P 618784
22.2%
H 309392
11.1%
U 309392
11.1%
A 309392
11.1%
2 259072
 
9.3%
7 172762
 
6.2%
6 46730
 
1.7%
1 39754
 
1.4%
8 29797
 
1.1%
Other values (4) 36783
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2784528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 652670
23.4%
P 618784
22.2%
H 309392
11.1%
U 309392
11.1%
A 309392
11.1%
2 259072
 
9.3%
7 172762
 
6.2%
6 46730
 
1.7%
1 39754
 
1.4%
8 29797
 
1.1%
Other values (4) 36783
 
1.3%

Interactions

2025-08-20T16:11:46.547693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.506051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.092079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.485903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.861903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.257013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.633225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.071422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.594729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.564249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.140986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.533044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.912157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.303447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.686227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.123370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.645844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.650245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.192177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.583685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.963492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.350753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.749154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.180223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.693815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.716228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.238736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.627329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.011072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.395063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.809159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.291695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.746594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.792381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.289596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.676635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.061511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.444711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.864157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.347162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.789814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.837895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.336044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.719997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.108230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.487842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.912815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.396776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.835710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:43.884217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.384552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.765154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.156135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.531929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.967295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.446087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.884678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.043022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.436245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:44.815322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.209054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:45.583690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.022364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T16:11:46.497634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-08-20T16:11:48.540770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Patient_IDUnnamed: 0basal_ratebolus_volume_deliveredcaloriescarb_inputglucoseheart_ratesteps
Patient_ID1.0000.6380.4280.0310.0720.0420.1430.1490.068
Unnamed: 00.6381.000-0.022-0.011-0.107-0.0220.0640.1510.046
basal_rate0.428-0.0221.0000.0300.1330.020-0.031-0.0560.017
bolus_volume_delivered0.031-0.0110.0301.0000.0510.4640.0390.0330.044
calories0.072-0.1070.1330.0511.0000.051-0.0190.4400.666
carb_input0.042-0.0220.0200.4640.0511.000-0.0360.0300.038
glucose0.1430.064-0.0310.039-0.019-0.0361.0000.1190.075
heart_rate0.1490.151-0.0560.0330.4400.0300.1191.0000.525
steps0.0680.0460.0170.0440.6660.0380.0750.5251.000

Missing values

2025-08-20T16:11:46.953097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-20T16:11:47.129482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0timeglucosecaloriesheart_ratestepsbasal_ratebolus_volume_deliveredcarb_inputPatient_ID
002020-01-17 00:00:0040.00000015.042996.3719018.00.0350.01.0HUPA0023P
112020-01-17 00:05:0041.3333338.316491.3953490.00.0350.00.0HUPA0023P
222020-01-17 00:10:0042.6666677.582685.9919350.00.0350.00.0HUPA0023P
332020-01-17 00:15:0044.0000007.338082.4344260.00.0350.00.0HUPA0023P
442020-01-17 00:20:0050.0000007.582678.8225810.00.0350.00.0HUPA0023P
552020-01-17 00:25:0056.0000007.582681.1015620.00.0350.00.0HUPA0023P
662020-01-17 00:30:0062.0000007.338071.9416670.00.0350.00.0HUPA0023P
772020-01-17 00:35:0071.6666677.704976.6880000.00.0350.00.0HUPA0023P
882020-01-17 00:40:0081.33333311.007073.7938930.00.0350.00.0HUPA0023P
992020-01-17 00:45:0091.0000006.604271.1718750.00.0350.00.0HUPA0023P
Unnamed: 0timeglucosecaloriesheart_ratestepsbasal_ratebolus_volume_deliveredcarb_inputPatient_ID
3093823093822019-07-13 17:55:0063.66666718.5484883.790323135.00.1520.00.0HUPA0020P
3093833093832019-07-13 18:00:0062.00000014.7740881.641509115.00.1520.00.0HUPA0020P
3093843093842019-07-13 18:05:0061.3333338.0880080.0444440.00.1520.00.0HUPA0020P
3093853093852019-07-13 18:10:0060.6666676.6860882.4360900.00.1520.00.0HUPA0020P
3093863093862019-07-13 18:15:0060.0000009.8134481.5590550.00.1520.00.0HUPA0020P
3093873093872019-07-13 18:20:0070.00000013.5878485.12396761.00.1520.00.0HUPA0020P
3093883093882019-07-13 18:25:0080.0000006.5782481.8861790.00.1520.00.0HUPA0020P
3093893093892019-07-13 18:30:0090.0000006.9017684.0461540.00.1520.00.0HUPA0020P
3093903093902019-07-13 18:35:00108.6666676.4704082.1102360.00.1520.00.0HUPA0020P
3093913093912019-07-13 18:40:00127.3333335.6076885.6969700.00.1520.00.0HUPA0020P